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Record W4405497063 · doi:10.1016/j.jacadv.2024.101451

Novel Therapies to Reduce Rehospitalization Risk in Worsening Heart Failure

2024· article· en· W4405497063 on OpenAlexaff
Ivna G C V Lima, Jairo Tavares Nunes, Lucas C. Godoy, Michael McDonald, Edimar Alcides Bocchi

Bibliographic record

VenueJACC Advances · 2024
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsTed Rogers Centre for Heart ResearchUniversity of Toronto
Fundersnot available
KeywordsHeart failureMedicineIntensive care medicineCardiologyInternal medicine

Abstract

fetched live from OpenAlex

Background: Worsening heart failure (WHF) challenges health care with frequent rehospitalizations and reduced quality of life for patients. Despite therapeutic advances, high rehospitalization risks highlight the urgent need for new treatments. Objectives: This study evaluated the effectiveness of initiating novel therapies during hospitalization or vulnerable phase for WHF patients to reduce rehospitalization risks and determine the optimal treatment sequence. Methods: A systematic review and network meta-analysis were performed in accordance with Cochrane Collaboration and Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. We included randomized clinical trials from January 2013, to December 2022, sourced from PUBMED and EMBASE, comparing novel heart failure therapies against control. The primary outcome was heart failure rehospitalization. Results: = 0.016). These therapies, when initiated during hospitalization, markedly influenced rehospitalization outcomes. Conclusions: Early administration of sodium-glucose co-transporter 2 inhibitors, ARNI, and ferric carboxymaltose for WHF patients significantly reduces rehospitalization risk. Our findings support a strategic shift in WHF management, advocating for the rapid introduction of these novel therapies to enhance patient prognosis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.841
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.304
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2024
Admission routes1
Has abstractyes

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